Transcript History of Artificial Intelligence

History of Artificial Intelligence
Dana Nejedlová
Department of Informatics
Faculty of Economics
Technical University of Liberec
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What is Intelligence?
• Common definition of artificial intelligence:
– AI is a field which attempts to build intelligent
machines and tries to understand intelligent entities.
• But what is intelligence?
– Learning, manipulating with facts, but also creativity,
consciousness, emotion and intuition.
• Can machines be intelligent?
– Up to the present day it is not sure whether it is
possible to build a machine that has all aspects of
intelligence.
– This kind of research is central in the field of AI.
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What Is Artificial Intelligence?
• Building machines that are able of symbolic processing,
recognition, learning, and other forms of inference
• Solving problems that must use heuristic search instead
of analytic approach
• Using inexact, missing, or poorly defined information
– Finding representational formalisms to compensate this
• Reasoning about significant qualitative features of a
situation
• Working with syntax and semantics
• Finding answers that are neither exact nor optimal but in
some sense „sufficient“
• The use of large amounts of domain-specific knowledge
• The use of meta-level knowledge (knowledge about
knowledge) to effect more sophisticated control of
problem solving strategies
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Before the Creation of Electronic
Computers
• Ancient and medieval myths
– Talos, Pandora, Golem
• artificial men, robots
• Research in the antiquity till the 17th century
– Aristotle, Gottfried Wilhelm Leibniz
• automation of reasoning
– Thomas Hobbes, René Descartes
• mechanistic understanding of living beings
• 20th century, 1948
– Norbert Wiener – Cybernetics: Or the Control and
Communication in the Animal and the Machine.
• Intelligent behavior is the result of the feedback mechanism.
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The Beginnings of Electronic
Computers
• John Louis von Neumann (1903 – 1957)
– Von Neumann’s architecture of a computer
• Consultations on the EDVAC Project (1945)
– Game Theory (1944)
• It can be applied to the interacting intelligent agents.
– Cellular automata (1966)
• They have computational capacity.
• Alan Mathison Turing (1912 – 1954)
– Turing Machine (1936)
• formalization of algorithm, abstraction of computer
– Turing Test (1950)
• proposal how to test the ability of a machine to demonstrate
thinking
– Programming of “Manchester Mark I” computer
(1949)
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The birth of “Artificial Intelligence”
• John McCarthy used the term “Artificial
Intelligence” for the first time as the topic of the
Dartmouth conference in 1956.
– Venue:
• Dartmouth College, Hanover, state New Hamphshire, USA
– Organizers:
• John McCarthy, Marvin Minsky, Nathaniel Rochester, and
Claude Shannon
– Participants:
• Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur
Samuel, Herbert Simon, and Allen Newell
– Proposal:
• To prove that every aspect of learning or any other feature of
intelligence can be so precisely described that a machine can
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be made to simulate it.
Approaches to Artificial Intelligence
• Good Old-fashioned Artificial Intelligence (GOFAI) or
symbolic artificial intelligence (John Haugeland, 1985)
– Program (e.g. classifier) in the GOFAI style is composed of parts
(e.g. rules), that have clear relation to the real world.
• New-fangled Artificial Intelligence
– The most important branch was connectionism – artificial neural
networks (McCulloch – Pitts, 1943).
– Genetic algorithms (Holland, 1975) and other kinds of
biologically inspired information processing
• Strong AI (John Searle, 1980)
– Artificial intelligence is real intelligence.
– Solution of complex problems, e.g. robotics.
• Weak AI
– Artificial intelligence is a mere imitation of human real
intelligence.
– Solution of a specific problems that do not cover the whole scale
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of human capabilities, e.g. OCR or chess.
Motivations for Biologically Inspired
Information Processing
• Danny Hillis: The Connection Machine (1985)
– Machines programmed in a GOFAI style tend to slow down as
they acquire more knowledge.
• They must search their knowledge base.
– Humans have the opposite property.
• They have massively parallel brain architecture.
– Humans were not produced by an engineering process.
• They are the result of evolution.
• Marvin Minsky: The Society of Mind (1986)
– Model of human intelligence which is built from the interactions
of simple parts called agents which are themselves mindless.
• It would be difficult to imagine how evolution could shape a single
system as complex as mind.
• Evolution could, however, shape individual specialized cognitive
units and form the mechanisms that enable the modules to interact.
• Marvin Minsky: The Emotion Machine (2006)
– Emotions are different ways to think that our mind uses to
increase our intelligence.
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Artificial Intelligence Philosophy
• What is intelligence and thinking?
– Turing test (1950)
– According to GOFAI thinking is symbol manipulation,
that is why program in the GOFAI style is thinking.
– Chinese Room Problem (John Searle, 1980)
• Thinking of humans and computers is different.
• Is human intelligence inseparable from mind and
emotions?
• In what sense can we say that a computer can
understand natural language?
• Who is responsible for the decisions made by
AI?
• What should be the ethics of people of dealing
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with the creations of artificial intelligence?
Hard Versus Soft Computing
• Good Old-fashioned Artificial Intelligence
– IF – THEN Rules
– Heuristics
• New-fangled Artificial Intelligence
– Neural networks
– Fuzzy logic
– Probabilistic reasoning
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belief networks (Bayes networks)
genetic algorithms
chaos theory
parts of learning theory (machine learning)
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Heuristics
• Problem-solving method that is usually successful, but
can fail i some situations
• Unclearly defined problems with missing or ambiguous
data
– Medical diagnosis
– Vision, speech recognition
• Helps to decide among infinite number of possible interpretations.
• A problem may have an exact solution, but the
computational cost of finding it may be prohibitive.
– Chess, tic-tac-toe, 15 or 8-puzzle, scheduling, path-finding…
– Heuristic evaluation function
• Evaluates each stage of solution.
– Number of conflicts in a number of possible schedules
• Helps to decide about the next step leading to the goal.
– Selecting the schedule with minimum number of conflicts for the next 11
small changes attempting to find some correct schedule
Expectations from Artificial
Intelligence
• Predictions of Herbert Simon and Allen
Newell (Heuristic Problem Solving, 1958),
that within ten years
– a digital computer will be the world's chess
champion,
– a digital computer will discover and prove an
important new mathematical theorem,
– a digital computer will compose critically
acclaimed music,
– most theories in psychology will take the form
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of computer programs.
Typical AI Problem
• 8 Queens Puzzle
• Is there a way of
placing 8 queens on
the chessboard so
that no two queens
would be able to
attack each other?
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Hard Problem for AI
• Truncated
Chessboard Problem
• Is there a way of
placing dominos on
the board so that
each square is
covered and each
domino covers
exactly two squares?
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Limitations of Artificial Intelligence
• David Hilbert (1862 – 1943) and
Kurt Gödel (1906 – 1978)
– Gödel‘s Incompleteness Theorem (1931)
• Consistency of a formal system cannot be proved within the
system, because it can contain statements with selfreference – logical paradoxes of the type:
– This statement is false.
– Some tasks have no algorithms.
• The halting problem
– It is not decidable whether the algorithm will halt or not.
– The algorithms in question contain again self-reference.
• Complexity Theory (NP-completeness, 1971)
– Some tasks have algorithms, but the computation
cannot be completed in practice (on a real computer),
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because it would take too much time.
Gödel‘s Incompleteness Theorem
• There are unprovable statements in every axiomatic
mathematical system expressive enough to define the
set of natural numbers.
• Example theorem 1 = 2
• Proof of the theorem:
• If a = b, a ≠ 0, b ≠ 0,
• then the two following equalities are also true:
a2 – b2 = (a – b) ∙ (a + b),
a2 – b2 = a2 – ab.
• And the following statements can be derived from them:
a2 – ab = (a – b) ∙ (a + b)
a ∙ (a – b) = (a – b) ∙ (a + b)
a=a+b
a=a+a
a = 2a
1=2
• Truth can be verified only when knowledge beyond the
natural finite numbers arithmetic is used.
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The Logic Theorist – The First
Artificial Intelligence Program
• Allen Newell, J.C. Shaw and Herbert Simon at
Carnegie Institute of Technology, now Carnegie
Mellon University, in 1955
• It did logic proofs from the book “Principia
Mathematica” (Bertrand Russell and Alfred North
Whitehead, 1910).
• It used mental processes of human experts.
– cognitive science
• To implement Logic Theorist on a computer, the
three researchers developed a programming
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language, IPL, a predecessor of Lisp.
Programming Languages
• Tasks like natural language processing,
knowledge representation, or theorem proving
needed a special language allowing processing
of symbolic data.
• Lisp (John McCarthy, USA, 1958)
– functional paradigm / list processing
• Program consists of functions of nested functions.
• Data and programs are represented the same way: a list.
– (+ 1 2 3) is a both a list of 4 atoms and a function returning
value 6.
• Program can serve as data for another program!
– Powerful feature allowing flexible and productive coding.
• Prolog (Alain Colmerauer, Europe, 1972)
– declarative paradigm / logic programming
• Program consists of facts and rules.
– Programmer describes (i.e. declares) a problem.
• Compiler deduces new facts from them.
– Programmer does not write the algorithm for the solution.
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Programs with Symbolic Artificial
Intelligence
• The General Problem Solver (1957)
– It was solving formalized symbolic problems, e.g.
mathematical proofs and chess.
• The Geometry Theorem Prover (1958)
– It was proving theorems with the help of explicitly
represented axioms.
• SAINT (Symbolic Automatic INTegrator)
– Integral calculus (1961)
• ANALOGY (1963)
– The picture A is to picture B like picture C to picture D.
• IQ tests are used for measuring the intelligence of people.
• Computers can be programmed to excel in IQ tests.
• But those programs would be stupid in real-world situations.
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Natural Language Processing
• STUDENT (1964, 1967)
– It was solving word problems in algebra.
• SIR (Semantic Information Retrieval, 1968)
– It was reading simple sentences and answered
questions.
• ELIZA (1965)
– It was simulating psychologist.
• TLC (Teachable Language Comprehender)
(1969)
– It was reading text and making semantic network.
• SUR (Speech Understanding Research) (1971)
– 5-year plan of the ARPA (today DARPA) agency of a 20
research in continuous speech recognition
Expert Systems
• They belong to the symbolic AI.
• They use a set of rules and heuristics.
• MACSYMA (MIT, 1968 -1982)
– It was doing symbolic math calculations.
• DENDRAL (SRI, 1965)
– It is identifying chemicals.
• MYCIN (SRI, Edward Shortliffe, 1974)
– It diagnosed infectious blood diseases.
– The following systems: EMYCIN, PUFF,
INTERNIST - CADUCEUS
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Commercial Expert Systems
• PROSPECTOR (SRI, 1974 – 1983)
– It is analyzing geological data and searching
for deposits of minerals.
• XCON – eXpert CONfigurer (CMU, 1978)
– It was configuring DEC’s VAX computers.
• TEIRESIAS (SRI, Randall Davis, 1976)
– Knowledge Acquisition System (KAS)
– It is acquiring knowledge from human experts.
– It is building knowledge bases for expert
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systems.
Robotics
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Marvin Lee Minsky (* 1927)
Freddy (University of Edinburgh,1973)
SHAKEY (SRI, 1969)
SHRDLU (MIT, Terry Winograd, 1970)
blocks worlds (MIT, 1970)
– Robot has to manipulate building blocks
according to instructions.
• computer vision
• natural language understanding
• planning
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The First Artificial Neural Networks
• Warren McCulloch and Walter Pitts
– Model of artificial neuron (1943)
– Neuron represents functions.
• Donald Olding Hebb
– Rule for neural network training (1949)
• Marvin Minsky and Dean Edmonds have
built the first computer with neural
network.
– SNARC (1951)
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Other Artificial Neural Networks
• Frank Rosenblatt
– Perceptron (1957)
• a single-layer network and its learning rule capable
of learning linearly separable functions
• Bernard Widrow and Marcian Ted Hoff
– Minimization of network’s root square error
– Delta rule (learning rule of a neural network)
– ADAptive LINEar Systems or neurons or
ADALINEs (1960)
– MADALINEs (1962)
• multi-layer versions of ADALINEs
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Neural Networks Critique
• Book „Perceptrons“ (Marvin Minsky and
Seymour Papert, 1969)
– When single-layer neural networks of a
Perceptron type cannot learn XOR function (it
is linearly inseparable), also multi-layer
networks cannot learn it.
– Hence funding of neural network research
was stopped until the beginning of the 20th
century 80’s.
• But multi-layer neural networks can learn
the XOR function.
• All that is needed for this is to find the right
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algorithm for their training.
Neural Networks Resurrection
• Hopfield net (John Hopfield, 1982)
– It can learn a couple of pictures (patterns).
• Self-Organizing Map (SOM) (Teuvo Kohonen, 1982)
– It can do unsupervised learning.
• Backpropagation (Arthur Bryson and Yu-Chi Ho, 1969)
– algorithm for training of a multilayer neural network
– It needs network’s neurons not to have a sharp threshold.
– Because it was not noticed, it was then rediscovered several
times in the 70’s and the 80’s of the 20th century and
popularized in 1986.
• NETtalk (Terry Sejnowski and Charles Rosenberg, 1986)
– Multi-layer neural network, that learned English pronunciation
and could generalize.
– It used the backpropagation algorithm.
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The Most Important AI Laboratories
• MIT (Massachusetts Institute of
Technology)
– 1959 - John McCarthy and Marvin Minsky
founded Artificial Intelligence Laboratory.
• SRI (Stanford Research Institute)
– 1963 - John McCarthy founded AI Laboratory.
• CMU (Carnegie Mellon University)
– 1980 - Raj Reddy founded The Robotics
Institute.
• IBM
• AT&T Bell Labs
• University of Edinburgh
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Present
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Robotic toys, space probes
Robotics in machinery
Home appliances (washers, vacuum cleaners)
Data Mining, fraud detection, spam filtering
Searching on the Internet (web agents)
Modeling of interactive processes (agents)
E-business – e-shops personalization
Intelligent tutoring systems and SW interfaces
Role-playing games, chess programs
Speech and video recognition
Machine translation
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